This Dataset shows some basic demographic data from the US census located around the San Francisco MSA at tract level. Attributes include Average age, female and male population, white population, hispanic population, population density, and total population.

Demographic Data for Boston’s Neighborhoods, 1950-2019

Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.

The American Community Survey (ACS) is an ongoing annual survey on a range of social, economic, demographic, and housing characteristics of the US population. For the purpose of our research study, we used the 5-year tabulations, which gave access to zipcode- and tract-level estimates of the variables.

This dataset provides highly detailed (Block Level) views of various demographics for Manhattan, New York city. this dataset includes information on age, race, sex, income, housing, and various other attributes. This data comes from the 2000 Us Census and was joined to the Census Tiger line files to create the output. enjoy!

The United Nations Energy Statistics Database (UNSTAT) is a comprehensive collection of international energy and demographic statistics prepared by the United Nations Statistics Division. The 2004 version represents the latest in the series of annual compilations which commenced under the title World Energy Supplies in Selected Years, 1929-1950. Supplementary series of monthly and quarterly data on production of energy may be found in the Monthly Bulletin of Statistics. The database contains comprehensive energy statistics for more than 215 countries or areas for production, trade and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. Mid-year population estimates are included to enable the computation of per capita data. Annual questionnaires sent to national statistical offices serve as the primary source of information. Supplementary data are also compiled from national, regional and international statistical publications. The Statistics Division prepares estimates where official data are incomplete or inconsistent. The database is updated on a continuous basis as new information and revisions are received. This metadata file represents the population statistics during the expressed time. For more information about the country site codes, click this link to the United Nations "Standard country or area codes for statistical use": https://unstats.un.org/unsd/methodology/m49/overview/

Vast records of our everyday interests and concerns are being generated by our frequent interactions with the Internet. Here, we investigate how the searches of Google users vary across U.S. states with different birth rates and infant mortality rates. We find that users in states with higher birth rates search for more information about pregnancy, while those in states with lower birth rates search for more information about cats. Similarly, we find that users in states with higher infant mortality rates search for more information about credit, loans and diseases. Our results provide evidence that Internet search data could offer new insight into the concerns of different demographics.

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This dataset is a boundary file obtained from the US Census Tiger Shape file library which can be found online. I downloaded the File for California then Used ESRI ArcMap to cut out the San Francisco MSA from Californai. Census demographic data was joined to the boundaries. This file includes attributes on Race and Populations and other demographic data.

This dataset provides a comprehensive analysis of global search engine market share trends, including regional variations and demographic insights. It covers the evolving landscape of search engine usage, privacy concerns, the rise of mobile-first and voice search, and how these changes are impacting digital marketing and SEO strategies.

This map contains NYC administrative boundaries enriched with various demographics datasets.Learn more about Esri's Enrich Layer / Geoenrichment analysis tool.Learn more about Esri's Demographics, Psychographic, and Socioeconomic datasets.Search for a specific location or site using the search bar. Toggle layer visibility with the layer list. Click on a layer to see more information about the feature.

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at August 2022 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England and Wales. Scotland and Northern Ireland has the 2011 Census Output AreasIt supports the production of area based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 184 MB).

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Introduction

Google Search Statistics: Google Search stands as the dominant global search engine, playing a crucial role in helping users find information, make informed decisions, and engage with online content. As digital habits evolve, Google remains at the forefront, influencing how people access websites, engage with brands, and discover new services.

Through advancements in AI, personalized results, and the growing trend of mobile searches, Google Search continuously refines its approach to deliver more relevant and efficient user experiences. Consequently, staying updated on Google Search trends and statistics is crucial for businesses, marketers, and analysts seeking to refine their strategies and increase visibility in a rapidly evolving digital landscape.

BatchData's Property Search API is trusted by organizations to power websites, applications, predictive models, and sales/marketing operations. The property search API boasts 300+ unique search filters enabling a granular and reliable property search experience.

Use Property Search API to identify properties within a certain buy-box, and combine with demographic data, MLS, mortgage data, and live events to surface motivated sellers and active buyers/borrowers. Or, search for properties who's homeowner information matches an ideal commercial or consumer profile.

Search by: - Owner Name - Property Assessed Value - Property Location - Building Characteristics - Household Demographics - Voluntary & Involuntary Liens - MLS Information - Sales, Transfer & Tax History - Owner Name

BatchData's Property Search API allows you to uncover the information you need.

1. Real Estate Market Data - this data helps users identify emerging trends, evaluate property values, and make informed investment decisions. Property Data
2. Detailed Property Data: The API offers in-depth details about individual properties, including square footage, number of bedrooms and bathrooms, lot size, and year built. It can also include information on property features such as pools, garages, and upgrades.
3. Demographic Data: The API can provide demographic information about the neighborhoods where properties are located, such as homeowner age, income, marital status and more. This helps users understand the community context and target market.

Key statistics from the Stanford GSB 2024 Search Fund Study, including counts, medians, and returns for search funds in the U.S. and Canada.

HAZUS is an abbreviation for Hazards United States, and was developed by FEMA. The HAZUS dataset was designed to estimate the potential physical, economic and social losses during hazardous events such as flooding or earthquakes. To measure the social impact of these events, HAZUS includes detailed demographic data for the United States. This dataset pulls out the income range data from the demographic files, at the census block level for the Kentucky section of the Cincinnati, Ohio Metropolitan Statistic Area (MSA). Income attributes include; incomes under $10k, incomes from $10k-$20k, $20k-$30k, and so on up until income of $100k+. Demographics data was recent as of May 2006.

Population prediction could provide effective data support for social and economic planning and decision-making, especially for the sub-national population forecasting accurately. In addition to realizing efficient smart population management, this research focuses primarily on the combination model for forecasting demographic data based on machine learning. As to the higher error of population forecasts due to high population density and mobility, a dynamic monitoring method based on mobile communication big data such as mobile phone signals is proposed, combined with more structurally stable traditional statistical data, it forms a multi-source dataset that possesses both accuracy and real-time characteristics. In the study, the Extreme Gradient Boosting tree (XGBoost) model is used to identify the base model to create a reliable predictive model for population dynamic monitoring. The sparrow search algorithm (SSA) is investigated to obtain more reasonable parameters of XGBoost to improve forecast accuracy. The combination model is verified based on the data of the 6th and 7th national population census and mobile phone signal data in Hebei Province, obtained the predicted data for mortality and migration, categorized by age and gender, for the following year. Subsequently, the research compared the performance of different metaheuristic algorithms and various gradient-boosting machine-learning models on the dataset. The SSA-XGBoost model demonstrates a better prediction performance in the demographic data forecast with better R2 0.9984 and a lower mean absolute error of 0.0002 and a mean squared error of 6.9184. The results of the comparative experiments and cross-validation show that the proposed predictive model can effectively forecast the demographic data for sub-national regions to realize smart population management.

As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

Context

Health care in the United States is provided by many distinct organizations. Health care facilities are largely owned and operated by private sector businesses. 58% of US community hospitals are non-profit, 21% are government owned, and 21% are for-profit. According to the World Health Organization (WHO), the United States spent more on healthcare per capita ($9,403), and more on health care as percentage of its GDP (17.1%), than any other nation in 2014. Many different datasets are needed to portray different aspects of healthcare in US like disease prevalences, pharmaceuticals and drugs, Nutritional data of different food products available in US. Such data is collected by surveys (or otherwise) conducted by Centre of Disease Control and Prevention (CDC), Foods and Drugs Administration, Center of Medicare and Medicaid Services and Agency for Healthcare Research and Quality (AHRQ). These datasets can be used to properly review demographics and diseases, determining start ratings of healthcare providers, different drugs and their compositions as well as package informations for different diseases and for food quality. We often want such information and finding and scraping such data can be a huge hurdle. So, Here an attempt is made to make available all US healthcare data at one place to download from in csv files.

Content

• Nhanes Survey (National Health and Nutrition Examination Survey) - The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. NHANES is a major program of the National Center for Health Statistics (NCHS). NCHS is part of the Centers for Disease Control and Prevention (CDC) and has the responsibility for producing vital and health statistics for the Nation. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel. The diseases, medical conditions, and health indicators to be studied include: Anemia, Cardiovascular disease, Diabetes, Environmental exposures, Eye diseases, Hearing loss, Infectious diseases, Kidney disease, Nutrition, Obesity, Oral health, Osteoporosis, Physical fitness and physical functioning, Reproductive history and sexual behavior, Respiratory disease (asthma, chronic bronchitis, emphysema), Sexually transmitted diseases, Vision. 10000 individuals are surveyed to represent US statistics. Five files in this datasets represent current recent Nhanes data -
  Nhanes_2005_2006.csv
  Nhanes_2007_2008.csv
  Nhanes_2009_2010.csv
  Nhanes_2011_2012.csv
  Nhanes_2013_2014.csv
  
  • Data fields' description -
    • Nhanes_2005_2006.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2007_2008.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2009_2010.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2011_2012.csv - Demographic, Dietary, [Examinations](http://https://wwwn.cdc.gov/nchs/nhanes/search/variab...

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.